Capstone Project : The Battle of Neighborhoods¶

Applied Data Science Capstone by IBM/Coursera¶

Table of contents¶

Introduction: Business Problem
Data
Crimes in Boston
Rents in Boston
Cluster of venues
Conclusion

Introduction: Business Problem ¶

In this project we will try to find an optimal location for a restaurant. Specifically, this report will be targeted to stakeholders interested in opening an restaurant in Boston, Massachusetts.

Since there are lots of restaurants in Massachusetts we will try to detect locations that are not already crowded with restaurants. We are also particularly interested in areas with low crime rate and areas that have reasonable rent.

We will use K-means clustering to segment and cluster the venues in each neighborhoods.

Data ¶

To cater this problem we will need the following data sets data set which are freely available.

Crimes in Boston from Kaggle which contains the data of all the crimes occured in different neighborhoods in boston in recent times.
Boston Rental Data from Zillow which is real estate website containing most up to date rental data in Boston Neighborhoods.
Boston Police Districts from Boston Police Department website. We need this because we need to encode District Abbrevations into Boston Neighborhoods

Crimes in Boston ¶

Import necessary packages¶

import numpy as np # library to handle data in a vectorized manner

import pandas as pd # library for data analsysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import json # library to handle JSON files


from geopy.geocoders import Nominatim # convert an address into latitude and longitude values

import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe

# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import seaborn as sns

# import k-means from clustering stage
from sklearn.cluster import KMeans


import folium # map rendering library

from zipfile import ZipFile
import requests
from bs4 import BeautifulSoup



print('Libraries imported.')

Libraries imported.

Read the Boston Crimes data set¶

#download and upload data manually
data1 = pd.read_csv('crime.csv')

/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

Utility Funtion to extract data from the web in the correct format¶

def DistrictData():

    url = 'https://bpdnews.com/districts'
    source = requests.get(url).text
    soup = BeautifulSoup(source,'lxml')

    links = soup.find_all('div', attrs={'class': 'sqs-block-content'})
    h2s = soup.find_all('h2')

    def getCodes(h2s):
        container = []
        i = 0
        for h2 in h2s:
            i = i + 1
            line = h2.find('strong').text
            if i == 1:
                pass
            elif i == 2:
                code = line.split(' ')
                container.append(code[1].replace('-',''))
                container.append(code[3].replace('-',''))
            else:
                code = line.split(' ')
                container.append(code[1].replace('-',''))
        return container

    def getNames(h2s):
            container = []
            i = 0 
            for h2 in h2s:
                i = i + 1
                x = h2.text.split('-')
                if i == 1:
                    pass
                elif i == 2:
                    item = x[2][2:]
                    names = item.split(' ')
                    container.append(names[0])
                    container.append(names[2])
                elif i == 7 or i == 9 or i == 11 or i == 12:
                    name = x[1][2:]
                    container.append(name)
                else:
                    name = x[1][1:]
                    container.append(name)
            
            return container

    Districts = getNames(h2s)
    Codes = getCodes(h2s)

    #data = pd.DataFrame({'Code' : Codes, 'Neighborhood' : Districts})
    return Districts, Codes
  
l2, l1 = DistrictData()
print(l1)
print(l2)

['A1', 'A15', 'A7', 'B2', 'B3', 'C6', 'C11', 'D4', 'D14', 'E5', 'E13', 'E18']
['Downtown', 'Charlestown', 'East Boston', 'Roxbury', 'Mattapan', 'South Boston', 'Dorchester', 'South End', 'Brighton', 'West Roxbury', 'Jamaica Plain', 'Hyde Park']

Replace the Distrct codes with names¶

data1.replace(l1,l2,inplace=True)
data1.head()

Find the total crimes in each neighborhood¶

crime_count = data1['DISTRICT'].value_counts()
a = crime_count.index
b = crime_count.values


crime_count = pd.DataFrame({'Neighborhood': a, 'Total' : b})
plot = crime_count.plot(kind = 'bar',x='Neighborhood',y='Total', figsize = (10,10))

fig = plot.get_figure()
fig.savefig("output.png")

Find the top 10 offenses in Boston¶

top10offense = data1['OFFENSE_CODE_GROUP'].value_counts()[:10,]

plt.figure(figsize=(10,5))
sns.barplot(top10offense.index, top10offense.values, alpha=0.8)
plt.title('Top 10 offenses')
plt.ylabel('Number of Occurrences', fontsize=12)
plt.xlabel('Offense', fontsize=12)
plt.xticks(
    rotation=45, 
    horizontalalignment='right',
    fontweight='light',
    #fontsize='x-large'  
)
plt.savefig('top10offense.png')
plt.show()

We can see that Larceny, otherwise known as theft, is very prolifent in Boston

Subset data for the year 2018 and offense of shoplifting¶

data2 = data1[data1['OFFENSE_CODE_GROUP'] == 'Larceny']
data2 = data2[data2['OFFENSE_DESCRIPTION']=='LARCENY SHOPLIFTING']
data2 = data2[data2['YEAR']==2018].reset_index(drop=True)
data2 = data2.iloc[0:500,:]
print(data2.shape)
data2.head()

(500, 17)

Extract the relevant Columns¶

data3 = data2[['DISTRICT','STREET','Lat','Long']].dropna()
#data3.rename(columns={'OFFENSE_CODE_GROUP': 'OFFENSE'}, inplace = True)
print(data3.shape)
data3.head()

(489, 4)

Create map and visualize¶

address = 'Boston, MA'

geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Boston are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of Boston are 42.3602534, -71.0582912.

boston_map = folium.Map(location=[latitude, longitude], zoom_start=12)

from folium import plugins

# let's start again with a clean copy of the map of San Francisco
boston_map = folium.Map(location = [latitude, longitude], zoom_start = 12)

# instantiate a mark cluster object for the incidents in the dataframe
incidents = plugins.MarkerCluster().add_to(boston_map)


# loop through the dataframe and add each data point to the mark cluster
for lat, lng, label, in zip(data3.Lat, data3.Long, data3['STREET']):
    folium.Marker(
        location=[lat, lng],
        icon=None,
        popup=label,
    ).add_to(incidents)

# display map
boston_map.save('fig1.html')
boston_map

Analysis¶

We can observe that most of the offenses are centered aound the South End area therefore, this region would not be ideal for openning a new restaurant

Rents in Boston Neighborhoods ¶

Download the Housing prices data set and observe raw data¶

housing_data = pd.read_csv('http://files.zillowstatic.com/research/public/Neighborhood/Neighborhood_ZriPerSqft_AllHomes.csv')
housing_data.head()

Make necessary changes in the dataset¶

rents = housing_data.loc[(housing_data['State'] == 'MA') & (housing_data['City']=='Boston'),['RegionName','2019-06']].reset_index(drop=True)
rents.rename(columns={'RegionName':'Neighborhood','2019-06':'Median price per sqft'}, inplace = True)
temp = rents.iloc[1:]
temp.replace('North Dorchester','Dorchester',inplace = True)
temp1 = temp.append({'Neighborhood':'Seaport District','Median price per sqft':3.577},ignore_index=True)
temp2 = temp1.append({'Neighborhood':'Longwood Medical Area','Median price per sqft':2.534},ignore_index=True)
rents = temp2

/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4042: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  method=method)

Utility Function for getting the coordinates of a neighborhood¶

#get the coordinates
def getCoordinates(address):
  geolocator = Nominatim(user_agent="ny_explorer")
  location = geolocator.geocode(address)
  latitude = location.latitude
  longitude = location.longitude
  return latitude,longitude

Get the coordinates and append in the dataframe¶

neighborhoods = rents['Neighborhood']

Latitudes = []
Longitudes = []

for n in neighborhoods:
  address = n + ', MA, USA'
  lat,lon = getCoordinates(address)
  #print(n + ' ' +str(lat)+','+str(lon))
  Latitudes.append(lat)
  Longitudes.append(lon)

rents['Lat'] = Latitudes
rents['Lon'] = Longitudes

rents.replace(['Seaport District','Longwood Medical Area'],['South Boston Waterfront','Longwood'],inplace= True)
rents

Download the Bostone GeoJson file for Constructing the Map¶

!wget --quiet http://bostonopendata-boston.opendata.arcgis.com/datasets/3525b0ee6e6b427f9aab5d0a1d0a1a28_0.geojson -O Boston.json
print('json file downloaded')

json file downloaded

Construct the Choropleth map of boston with respect to rents¶

boston_geo = r'Boston.json'
with open('Boston.json') as json_data:
    boston_data = json.load(json_data)

del(boston_data['features'][-1])

boston_map = folium.Map(location=[42.3602534, -71.0582912], zoom_start=12)

boston_map.choropleth(
    geo_data=boston_data,
    data=rents,
    columns=['Neighborhood', 'Median price per sqft'],
    key_on='feature.properties.Name',
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Housing Prices per sqft',
    reset=True
)
boston_map

/usr/local/lib/python3.6/dist-packages/folium/folium.py:426: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  FutureWarning

Analysis of Rents¶

As shown in the map, we observe that the regions in the southeren reghion have very high rent. Thus, these regions should not be chosen if there are budget constraints.

Clustering of neighborhoods ¶

Define credentials for Foursquare¶

CLIENT_ID = 'MWJ54SMBOGYZOKQPTV3ZDM3VUPROJXUDD5MK5BDWKKYSDTSS' # your Foursquare ID
CLIENT_SECRET = '5C1ZPNIZY2IQEL5CMN1AS22DED4EY0ILTZOTX40C4ACTE20Y' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version

print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)

Your credentails:
CLIENT_ID: MWJ54SMBOGYZOKQPTV3ZDM3VUPROJXUDD5MK5BDWKKYSDTSS
CLIENT_SECRET:5C1ZPNIZY2IQEL5CMN1AS22DED4EY0ILTZOTX40C4ACTE20Y

Get Nearby Venues¶

def getNearbyVenues(names, latitudes, longitudes, radius=1000, LIMIT=150):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        #print(name)
        
       
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

boston_venues = getNearbyVenues(names=rents['Neighborhood'],
                                latitudes=rents['Lat'],
                                longitudes=rents['Lon'])

Observe and analyze the data¶

Size of data¶

print(boston_venues.shape)
boston_venues.head()

(1726, 7)

How many venues were returned for each neighborhood¶

boston_venues.groupby('Neighborhood').count()

How many different categories were returned¶

print('There are {} uniques categories.'.format(len(boston_venues['Venue Category'].unique())))

There are 242 uniques categories.

Prepare data for clusering¶

One hot encodding for venues¶

# one hot encoding
boston_onehot = pd.get_dummies(boston_venues[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
boston_onehot['Neighborhood'] = boston_venues['Neighborhood'] 

# move neighborhood column to the first column
fixed_columns = [boston_onehot.columns[-1]] + list(boston_onehot.columns[:-1])
boston_onehot = boston_onehot[fixed_columns]

boston_onehot.head()

Observe the data¶

boston_grouped = boston_onehot.groupby('Neighborhood').mean().reset_index()
boston_grouped

Get the top 5 categories for each neighborhood¶

num_top_venues = 5

for hood in boston_grouped['Neighborhood']:
    print("----"+hood+"----")
    temp = boston_grouped[boston_grouped['Neighborhood'] == hood].T.reset_index()
    temp.columns = ['venue','freq']
    temp = temp.iloc[1:]
    temp['freq'] = temp['freq'].astype(float)
    temp = temp.round({'freq': 2})
    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
    print('\n')

----Allston----
                venue  freq
0         Pizza Place  0.06
1   Korean Restaurant  0.06
2  Chinese Restaurant  0.05
3              Bakery  0.05
4                 Bar  0.03


----Back Bay----
                 venue  freq
0   Italian Restaurant  0.04
1                Hotel  0.04
2          Coffee Shop  0.04
3       Ice Cream Shop  0.04
4  American Restaurant  0.04


----Bay Village----
                venue  freq
0         Coffee Shop  0.06
1                 Spa  0.06
2             Theater  0.05
3  Italian Restaurant  0.05
4  Chinese Restaurant  0.04


----Beacon Hill----
                 venue  freq
0   Italian Restaurant  0.06
1  American Restaurant  0.04
2                Hotel  0.04
3          Pizza Place  0.04
4          Coffee Shop  0.04


----Brighton----
           venue  freq
0    Pizza Place  0.08
1    Coffee Shop  0.05
2            Pub  0.05
3  Grocery Store  0.04
4     Donut Shop  0.04


----Charlestown----
            venue  freq
0            Park  0.06
1   Grocery Store  0.04
2  History Museum  0.04
3      Donut Shop  0.04
4            Café  0.04


----Chinatown----
                venue  freq
0    Asian Restaurant  0.07
1      Sandwich Place  0.06
2         Coffee Shop  0.06
3  Chinese Restaurant  0.04
4  Italian Restaurant  0.03


----Dorchester----
                  venue  freq
0           Pizza Place  0.12
1              Pharmacy  0.12
2                  Bank  0.06
3           Golf Course  0.06
4  Caribbean Restaurant  0.06


----Downtown----
            venue  freq
0            Café  0.06
1  Sandwich Place  0.06
2      Donut Shop  0.05
3             Bar  0.05
4       Rock Club  0.04


----East Boston----
                       venue  freq
0         Mexican Restaurant  0.12
1                       Park  0.09
2         Italian Restaurant  0.07
3                Pizza Place  0.07
4  Latin American Restaurant  0.07


----Fenway----
                    venue  freq
0     American Restaurant  0.05
1                    Café  0.04
2             Pizza Place  0.03
3                  Lounge  0.03
4  Furniture / Home Store  0.03


----Hyde Park----
                 venue  freq
0          Pizza Place  0.12
1        Grocery Store  0.09
2             Pharmacy  0.06
3         Home Service  0.06
4  American Restaurant  0.06


----Jamaica Plain----
         venue  freq
0         Park  0.09
1       Bakery  0.07
2  Coffee Shop  0.05
3  Pizza Place  0.05
4    Bookstore  0.03


----Kenmore----
                 venue  freq
0                 Farm   0.5
1         Liquor Store   0.5
2          Yoga Studio   0.0
3  Monument / Landmark   0.0
4        Movie Theater   0.0


----Longwood----
                 venue  freq
0                 Café  0.07
1          Pizza Place  0.06
2          Coffee Shop  0.06
3                 Park  0.04
4  American Restaurant  0.04


----Mattapan----
                  venue  freq
0            Donut Shop  0.09
1         Metro Station  0.05
2  Fast Food Restaurant  0.05
3                  Food  0.05
4  Gym / Fitness Center  0.05


----Mission Hill----
            venue  freq
0      Donut Shop  0.09
1            Café  0.07
2  Sandwich Place  0.06
3     Coffee Shop  0.04
4     Bus Station  0.04


----North End----
                venue  freq
0  Italian Restaurant  0.34
1                Park  0.06
2  Seafood Restaurant  0.05
3              Bakery  0.05
4         Pizza Place  0.05


----Roslindale----
                 venue  freq
0          Pizza Place  0.10
1        Grocery Store  0.07
2  American Restaurant  0.07
3          Yoga Studio  0.05
4   Italian Restaurant  0.05


----Roxbury----
                venue  freq
0          Donut Shop  0.13
1         Pizza Place  0.08
2   Convenience Store  0.05
3  Italian Restaurant  0.05
4        Skating Rink  0.05


----South Boston----
            venue  freq
0     Pizza Place  0.08
1             Bar  0.07
2      Donut Shop  0.05
3  Sandwich Place  0.05
4             Gym  0.04


----South Boston Waterfront----
                venue  freq
0               Hotel  0.09
1  Seafood Restaurant  0.08
2  Italian Restaurant  0.06
3          Steakhouse  0.05
4         Coffee Shop  0.04


----South End----
            venue  freq
0     Pizza Place  0.07
1          Casino  0.05
2      Donut Shop  0.05
3    Burger Joint  0.05
4  Sandwich Place  0.05


----West End----
                venue  freq
0  Italian Restaurant  0.08
1              Bakery  0.07
2         Pizza Place  0.05
3                Park  0.05
4  Seafood Restaurant  0.04


----West Roxbury----
                 venue  freq
0          Pizza Place  0.08
1             Pharmacy  0.08
2    Convenience Store  0.06
3         Liquor Store  0.06
4  American Restaurant  0.04

Get the top 10 Most common Venue¶

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = boston_grouped['Neighborhood']

for ind in np.arange(boston_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(boston_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted

Create Clusters of venues¶

# set number of clusters
kclusters = 7

boston_grouped_clustering = boston_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(boston_grouped_clustering)

#check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]

array([0, 1, 1, 1, 0, 0, 1, 6, 5, 5], dtype=int32)

# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

boston_merged = rents

# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
boston_merged = boston_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')

boston_merged.head() # check the last columns!

Observe Boston Rents with Clusters¶

boston_map = folium.Map(location=[42.3602534, -71.0582912], zoom_start=12)

boston_map.choropleth(
    geo_data=boston_data,
    data=rents,
    columns=['Neighborhood', 'Median price per sqft'],
    key_on='feature.properties.Name',
    fill_color='YlOrBr', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Housing Prices per sqft',
    highlight = True,
    reset=True
)


# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, neigh, price, venue, cluster in zip(boston_merged['Lat'], boston_merged['Lon'], boston_merged['Neighborhood'], boston_merged['Median price per sqft'], boston_merged['1st Most Common Venue'], boston_merged['Cluster Labels']):
    label = folium.Popup(str(neigh)+'\n'+ 
                         'Cluster:'+ 
                         str(cluster)+'\n'+
                         'Avg Price:'+ 
                         str(format(price,'.2f'))+'\n'+ 
                         'Venue:'+
                         str(venue)+'\n', 
                         parse_html = True,
                         max_width = '200%')
    folium.CircleMarker(
        [lat, lon],
        radius=6,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(boston_map)
       
boston_map

/usr/local/lib/python3.6/dist-packages/folium/folium.py:426: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  FutureWarning

Let's view the clusters¶

def top3(cluster):
  temp = cluster.iloc[:,1:4].apply(pd.value_counts)
  temp = temp.sum(axis = 1).sort_values(ascending = False)
  venue = temp.index[0:3]
  count = temp.values[0:3]
  count = [int(i) for i in count]
 
  for i in range(3):
    label = str(count[i])+ " "+  venue[i]
    print(label)
  return venue,count

Cluster 1¶

cluster1 = boston_merged.loc[boston_merged['Cluster Labels'] == 0, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster1)

6 Pizza Place
2 Park
2 Grocery Store

Cluster 2¶

cluster2 = boston_merged.loc[boston_merged['Cluster Labels'] == 1, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster2)

5 Italian Restaurant
3 Pizza Place
2 Seafood Restaurant

Cluster 3¶

cluster3 = boston_merged.loc[boston_merged['Cluster Labels'] == 2, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster3)

1 Women's Store
1 Liquor Store
1 Farm

Cluster 4¶

cluster4 = boston_merged.loc[boston_merged['Cluster Labels'] == 3, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster4)

1 Women's Store
1 Hardware Store
1 Donut Shop

Cluster 5¶

cluster5 = boston_merged.loc[boston_merged['Cluster Labels'] == 4, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster5)

1 Pizza Place
1 Park
1 Italian Restaurant

Cluster 6¶

cluster5 = boston_merged.loc[boston_merged['Cluster Labels'] == 5, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster5)

4 Café
3 Pizza Place
3 Donut Shop

Cluster 7¶

cluster6 = boston_merged.loc[boston_merged['Cluster Labels'] == 6, boston_merged.columns[[1] + list(range(5, boston_merged.shape[1]))]].reset_index(drop=True)
a,b = top3(cluster6)

1 Pizza Place
1 Pharmacy
1 Fried Chicken Joint

Conclusion ¶

As per our analysis we can see the southeren region of Boston have the highest number of cluster centers. This indicates greater chances of prosperous business. However, there is also a tradeoff that comes in terms of high rent. In addition, there are greater occurences of crimes in that region. In a nutshell, there is plenty of information available for law enforcement agencies, real-estate agents and ctiy councils to improve standards in the city

	INCIDENT_NUMBER	OFFENSE_CODE	OFFENSE_CODE_GROUP	OFFENSE_DESCRIPTION	DISTRICT	REPORTING_AREA	SHOOTING	OCCURRED_ON_DATE	YEAR	MONTH	DAY_OF_WEEK	HOUR	UCR_PART	STREET	Lat	Long	Location
0	I182080058	2403	Disorderly Conduct	DISTURBING THE PEACE	Hyde Park	495	NaN	10/3/2018 20:13	2018	10	Wednesday	20	Part Two	ARLINGTON ST	42.262608	-71.121186	(42.26260773, -71.12118637)
1	I182080053	3201	Property Lost	PROPERTY - LOST	Brighton	795	NaN	8/30/2018 20:00	2018	8	Thursday	20	Part Three	ALLSTON ST	42.352111	-71.135311	(42.35211146, -71.13531147)
2	I182080052	2647	Other	THREATS TO DO BODILY HARM	Roxbury	329	NaN	10/3/2018 19:20	2018	10	Wednesday	19	Part Two	DEVON ST	42.308126	-71.076930	(42.30812619, -71.07692974)
3	I182080051	413	Aggravated Assault	ASSAULT - AGGRAVATED - BATTERY	Downtown	92	NaN	10/3/2018 20:00	2018	10	Wednesday	20	Part One	CAMBRIDGE ST	42.359454	-71.059648	(42.35945371, -71.05964817)
4	I182080050	3122	Aircraft	AIRCRAFT INCIDENTS	East Boston	36	NaN	10/3/2018 20:49	2018	10	Wednesday	20	Part Three	PRESCOTT ST	42.375258	-71.024663	(42.37525782, -71.02466343)

	INCIDENT_NUMBER	OFFENSE_CODE	OFFENSE_CODE_GROUP	OFFENSE_DESCRIPTION	DISTRICT	REPORTING_AREA	SHOOTING	OCCURRED_ON_DATE	YEAR	MONTH	DAY_OF_WEEK	HOUR	UCR_PART	STREET	Lat	Long	Location
0	I182080029	613	Larceny	LARCENY SHOPLIFTING	South End	146	NaN	10/3/2018 19:09	2018	10	Wednesday	19	Part One	HUNTINGTON AVE	42.347972	-71.078496	(42.34797240, -71.07849582)
1	I182080003	613	Larceny	LARCENY SHOPLIFTING	South Boston	214	NaN	10/3/2018 17:45	2018	10	Wednesday	17	Part One	W BROADWAY	42.336112	-71.046654	(42.33611200, -71.04665434)
2	I182079973	613	Larceny	LARCENY SHOPLIFTING	Downtown	94	NaN	10/3/2018 13:40	2018	10	Wednesday	13	Part One	FANEUIL HALL MARKETPLACE	42.359980	-71.054741	(42.35997967, -71.05474135)
3	I182079942	613	Larceny	LARCENY SHOPLIFTING	Downtown	103	NaN	10/3/2018 14:47	2018	10	Wednesday	14	Part One	WASHINGTON ST	42.356801	-71.058930	(42.35680075, -71.05893025)
4	I182079899	613	Larceny	LARCENY SHOPLIFTING	Mattapan	427	NaN	10/3/2018 13:38	2018	10	Wednesday	13	Part One	BLUE HILL AVE	42.274701	-71.093441	(42.27470119, -71.09344053)

	DISTRICT	STREET	Lat	Long
0	South End	HUNTINGTON AVE	42.347972	-71.078496
1	South Boston	W BROADWAY	42.336112	-71.046654
2	Downtown	FANEUIL HALL MARKETPLACE	42.359980	-71.054741
3	Downtown	WASHINGTON ST	42.356801	-71.058930
4	Mattapan	BLUE HILL AVE	42.274701	-71.093441

	RegionID	RegionName	City	State	Metro	CountyName	SizeRank	2010-11	2010-12	2011-01	2011-02	2011-03	2011-04	2011-05	2011-06	2011-07	2011-08	2011-09	2011-10	2011-11	2011-12	2012-01	2012-02	2012-03	2012-04	2012-05	2012-06	2012-07	2012-08	2012-09	2012-10	2012-11	2012-12	2013-01	2013-02	2013-03	2013-04	2013-05	2013-06	2013-07	2013-08	2013-09	2013-10	2013-11	2013-12	2014-01	2014-02	2014-03	2014-04	2014-05	2014-06	2014-07	2014-08	2014-09	2014-10	2014-11	2014-12	2015-01	2015-02	2015-03	2015-04	2015-05	2015-06	2015-07	2015-08	2015-09	2015-10	2015-11	2015-12	2016-01	2016-02	2016-03	2016-04	2016-05	2016-06	2016-07	2016-08	2016-09	2016-10	2016-11	2016-12	2017-01	2017-02	2017-03	2017-04	2017-05	2017-06	2017-07	2017-08	2017-09	2017-10	2017-11	2017-12	2018-01	2018-02	2018-03	2018-04	2018-05	2018-06	2018-07	2018-08	2018-09	2018-10	2018-11	2018-12	2019-01	2019-02	2019-03	2019-04	2019-05	2019-06
0	274772	Northeast Dallas	Dallas	TX	Dallas-Fort Worth-Arlington	Dallas County	1	0.844	0.850	0.858	0.862	0.862	0.858	0.856	0.858	0.860	0.862	0.864	0.862	0.862	0.858	0.854	0.854	0.856	0.860	0.864	0.872	0.882	0.892	0.900	0.904	0.904	0.904	0.904	0.908	0.912	0.918	0.926	0.934	0.942	0.944	0.952	0.958	0.966	0.968	0.972	0.976	0.978	0.980	0.982	0.986	0.994	1.010	1.024	1.032	1.036	1.036	1.036	1.036	1.038	1.042	1.052	1.068	1.084	1.098	1.106	1.110	1.112	1.110	1.112	1.110	1.114	1.120	1.132	1.144	1.152	1.160	1.164	1.168	1.170	1.168	1.164	1.162	1.160	1.162	1.168	1.176	1.188	1.196	1.202	1.202	1.200	1.196	1.194	1.192	1.190	1.188	1.186	1.186	1.186	1.186	1.186	1.188	1.196	1.208	1.216	1.220	1.220	1.222	1.224	1.230
1	112345	Maryvale	Phoenix	AZ	Phoenix-Mesa-Scottsdale	Maricopa County	2	0.672	0.670	0.666	0.664	0.664	0.662	0.660	0.658	0.656	0.656	0.654	0.652	0.650	0.646	0.642	0.638	0.636	0.636	0.640	0.646	0.650	0.650	0.650	0.646	0.646	0.646	0.648	0.646	0.648	0.650	0.652	0.654	0.656	0.658	0.660	0.658	0.658	0.660	0.662	0.662	0.660	0.660	0.662	0.666	0.672	0.676	0.678	0.680	0.682	0.688	0.692	0.696	0.698	0.702	0.704	0.710	0.718	0.726	0.728	0.724	0.718	0.714	0.716	0.726	0.738	0.752	0.764	0.780	0.790	0.790	0.788	0.786	0.784	0.778	0.774	0.768	0.766	0.768	0.776	0.788	0.802	0.812	0.820	0.824	0.826	0.826	0.826	0.826	0.832	0.836	0.838	0.842	0.844	0.846	0.848	0.850	0.862	0.876	0.888	0.896	0.898	0.900	0.900	0.904
2	192689	Paradise	Las Vegas	NV	Las Vegas-Henderson-Paradise	Clark County	3	0.758	0.756	0.754	0.752	0.750	0.748	0.746	0.744	0.744	0.746	0.744	0.742	0.740	0.736	0.734	0.732	0.728	0.728	0.726	0.728	0.730	0.730	0.728	0.726	0.724	0.724	0.722	0.722	0.722	0.724	0.724	0.726	0.726	0.728	0.730	0.732	0.732	0.730	0.728	0.728	0.728	0.728	0.730	0.730	0.734	0.736	0.740	0.742	0.742	0.742	0.742	0.742	0.744	0.748	0.750	0.754	0.760	0.764	0.766	0.766	0.764	0.762	0.764	0.768	0.772	0.776	0.778	0.782	0.782	0.786	0.786	0.786	0.786	0.786	0.786	0.786	0.788	0.790	0.796	0.800	0.806	0.810	0.814	0.820	0.824	0.828	0.830	0.830	0.832	0.834	0.836	0.838	0.838	0.838	0.840	0.842	0.854	0.870	0.884	0.888	0.892	0.896	0.902	0.912
3	270958	Upper West Side	New York	NY	New York-Newark-Jersey City	New York County	4	4.456	4.470	4.466	4.450	4.392	4.302	4.224	4.160	4.138	4.098	4.092	4.046	4.014	3.986	3.990	4.020	4.044	4.056	4.068	4.018	4.038	4.062	4.118	4.156	4.196	4.210	4.150	4.074	4.058	4.098	4.150	4.218	4.242	4.208	4.164	4.170	4.234	4.326	4.404	4.476	4.522	4.542	4.536	4.528	4.532	4.556	4.604	4.688	4.782	4.836	4.880	4.912	4.938	4.958	4.974	4.994	5.020	5.062	5.096	5.102	5.080	5.058	5.042	5.042	5.046	5.070	5.082	5.086	5.066	5.050	5.036	5.022	5.016	5.012	5.012	5.012	5.014	5.014	5.012	5.006	5.006	5.006	5.010	5.002	4.986	4.958	4.942	4.934	4.934	4.930	4.920	4.908	4.902	4.900	4.900	4.912	4.944	4.978	5.008	5.026	5.048	5.076	5.152	5.234
4	118208	South Los Angeles	Los Angeles	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	5	1.416	1.412	1.410	1.414	1.414	1.410	1.406	1.400	1.404	1.404	1.408	1.412	1.416	1.412	1.402	1.394	1.394	1.396	1.400	1.394	1.386	1.370	1.360	1.356	1.356	1.364	1.380	1.400	1.416	1.426	1.438	1.442	1.440	1.434	1.432	1.442	1.456	1.464	1.462	1.456	1.446	1.452	1.454	1.456	1.456	1.466	1.496	1.522	1.556	1.580	1.604	1.620	1.636	1.642	1.644	1.646	1.654	1.662	1.666	1.672	1.672	1.682	1.696	1.716	1.724	1.734	1.738	1.746	1.750	1.748	1.752	1.760	1.778	1.804	1.828	1.850	1.868	1.886	1.898	1.910	1.924	1.940	1.954	1.962	1.964	1.962	1.964	1.972	1.984	1.990	1.990	1.984	1.988	1.994	1.998	2.008	2.024	2.050	2.070	2.080	2.082	2.082	2.082	2.082

	Neighborhood	Median price per sqft	Lat	Lon
0	Roxbury	1.914	42.324843	-71.095016
1	Jamaica Plain	2.360	42.309820	-71.120330
2	East Boston	2.442	42.375097	-71.039217
3	Brighton	2.896	42.350097	-71.156442
4	Mattapan	1.896	42.272321	-71.086995
5	South Boston	2.994	42.333431	-71.049495
6	Hyde Park	1.984	42.255654	-71.124496
7	West Roxbury	1.940	42.279265	-71.149497
8	South End	3.670	42.095928	-72.582866
9	Roslindale	1.992	42.291209	-71.124497
10	Dorchester	2.286	42.297320	-71.074495
11	Allston	3.018	42.355434	-71.132127
12	Back Bay	4.088	42.350707	-71.079730
13	Fenway	4.216	42.343451	-71.097716
14	Charlestown	2.838	42.377875	-71.061996
15	Kenmore	4.084	39.692611	-75.867163
16	Mission Hill	2.524	42.332926	-71.103214
17	North End	3.734	42.365097	-71.054495
18	Beacon Hill	4.260	42.359820	-71.066162
19	Chinatown	4.002	42.352217	-71.062607
20	West End	3.290	42.363919	-71.063899
21	Downtown	3.592	42.263069	-71.802002
22	Bay Village	3.984	42.350011	-71.066948
23	South Boston Waterfront	3.577	42.348765	-71.043988
24	Longwood	2.534	42.336168	-71.099527

	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	Allston	Pizza Place	Korean Restaurant	Chinese Restaurant	Bakery	Bar	Gym / Fitness Center	Thai Restaurant	Coffee Shop	Sushi Restaurant	Rock Club
1	Back Bay	Italian Restaurant	American Restaurant	Seafood Restaurant	Hotel	Coffee Shop	Ice Cream Shop	Spa	Gym / Fitness Center	Cosmetics Shop	Gym
2	Bay Village	Coffee Shop	Spa	Italian Restaurant	Theater	Sandwich Place	Asian Restaurant	Chinese Restaurant	Gym	Park	Cycle Studio
3	Beacon Hill	Italian Restaurant	Hotel	Pizza Place	American Restaurant	Coffee Shop	Historic Site	Seafood Restaurant	Steakhouse	Gym / Fitness Center	Bakery
4	Brighton	Pizza Place	Coffee Shop	Pub	Grocery Store	Donut Shop	Convenience Store	Chinese Restaurant	Café	Bakery	Thai Restaurant
5	Charlestown	Park	Café	Gastropub	Grocery Store	Donut Shop	History Museum	Pizza Place	National Park	Coffee Shop	Gym
6	Chinatown	Asian Restaurant	Coffee Shop	Sandwich Place	Chinese Restaurant	Italian Restaurant	Theater	Hotel	New American Restaurant	Gastropub	Seafood Restaurant
7	Dorchester	Pizza Place	Pharmacy	Fried Chicken Joint	Mobile Phone Shop	Platform	Fast Food Restaurant	Discount Store	Park	Market	Sandwich Place
8	Downtown	Café	Sandwich Place	Bar	Donut Shop	American Restaurant	Rock Club	Italian Restaurant	Pizza Place	Mexican Restaurant	Coffee Shop
9	East Boston	Mexican Restaurant	Park	Italian Restaurant	Latin American Restaurant	Pizza Place	Café	Liquor Store	Sandwich Place	Seafood Restaurant	Burrito Place
10	Fenway	American Restaurant	Café	Thai Restaurant	Furniture / Home Store	Restaurant	Hotel	Burger Joint	Pizza Place	Baseball Field	Lounge
11	Hyde Park	Pizza Place	Grocery Store	Park	Home Service	Pharmacy	American Restaurant	Bar	Theater	Storage Facility	Business Service
12	Jamaica Plain	Park	Bakery	Pizza Place	Coffee Shop	Thrift / Vintage Store	Ice Cream Shop	New American Restaurant	Pub	Bookstore	Yoga Studio
13	Kenmore	Farm	Liquor Store	Women's Store	Food	Fish Market	Fast Food Restaurant	Farmers Market	Falafel Restaurant	Electronics Store	Dumpling Restaurant
14	Longwood	Café	Pizza Place	Coffee Shop	American Restaurant	Park	Sandwich Place	Thai Restaurant	Greek Restaurant	Furniture / Home Store	Donut Shop
15	Mattapan	Donut Shop	Women's Store	Hardware Store	Playground	Pizza Place	Pharmacy	Mobile Phone Shop	Metro Station	Liquor Store	Ice Cream Shop
16	Mission Hill	Donut Shop	Café	Sandwich Place	Pizza Place	Coffee Shop	Art Museum	Park	Bus Station	Gym	Italian Restaurant
17	North End	Italian Restaurant	Park	Pizza Place	Bakery	Seafood Restaurant	Café	Historic Site	Sandwich Place	Market	Coffee Shop
18	Roslindale	Pizza Place	American Restaurant	Grocery Store	Yoga Studio	Plaza	Italian Restaurant	Bar	Sandwich Place	Discount Store	Liquor Store
19	Roxbury	Donut Shop	Pizza Place	Skating Rink	Convenience Store	Italian Restaurant	Burger Joint	Sandwich Place	Park	Liquor Store	Mobile Phone Shop
20	South Boston	Pizza Place	Bar	Donut Shop	Sandwich Place	Coffee Shop	Gym	Italian Restaurant	Gym / Fitness Center	Park	Dive Bar
21	South Boston Waterfront	Hotel	Seafood Restaurant	Italian Restaurant	Steakhouse	Coffee Shop	Asian Restaurant	Mexican Restaurant	Gym	Brewery	Bar
22	South End	Pizza Place	Shipping Store	Donut Shop	Casino	Sports Bar	Burger Joint	Sandwich Place	Hotel	American Restaurant	Coffee Shop
23	West End	Italian Restaurant	Bakery	Pizza Place	Park	Seafood Restaurant	Coffee Shop	Historic Site	Hotel	Yoga Studio	Science Museum
24	West Roxbury	Pizza Place	Pharmacy	Convenience Store	Liquor Store	Bank	Grocery Store	American Restaurant	Park	Spa	Nail Salon

	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
Neighborhood
Allston	87	87	87	87	87	87
Back Bay	100	100	100	100	100	100
Bay Village	100	100	100	100	100	100
Beacon Hill	100	100	100	100	100	100
Brighton	79	79	79	79	79	79
Charlestown	69	69	69	69	69	69
Chinatown	100	100	100	100	100	100
Dorchester	16	16	16	16	16	16
Downtown	100	100	100	100	100	100
East Boston	43	43	43	43	43	43
Fenway	100	100	100	100	100	100
Hyde Park	33	33	33	33	33	33
Jamaica Plain	58	58	58	58	58	58
Kenmore	2	2	2	2	2	2
Longwood	100	100	100	100	100	100
Mattapan	22	22	22	22	22	22
Mission Hill	70	70	70	70	70	70
North End	100	100	100	100	100	100
Roslindale	41	41	41	41	41	41
Roxbury	38	38	38	38	38	38
South Boston	73	73	73	73	73	73
South Boston Waterfront	100	100	100	100	100	100
South End	43	43	43	43	43	43
West End	100	100	100	100	100	100
West Roxbury	52	52	52	52	52	52

	Neighborhood	Yoga Studio	ATM	Accessories Store	Afghan Restaurant	African Restaurant	American Restaurant	Antique Shop	Art Gallery	Art Museum	Arts & Crafts Store	Asian Restaurant	Athletics & Sports	Auditorium	Automotive Shop	BBQ Joint	Bagel Shop	Bakery	Bank	Bar	Baseball Field	Baseball Stadium	Basketball Court	Beach	Bed & Breakfast	Beer Bar	Beer Garden	Beer Store	Belgian Restaurant	Big Box Store	Bike Rental / Bike Share	Bistro	Board Shop	Boat or Ferry	Bookstore	Boutique	Bowling Alley	Boxing Gym	Brazilian Restaurant	Breakfast Spot	Brewery	Bubble Tea Shop	Building	Burger Joint	Burrito Place	Bus Station	Bus Stop	Business Service	Café	Caribbean Restaurant	Casino	Cemetery	Cheese Shop	Child Care Service	Chinese Restaurant	Chocolate Shop	Church	Circus	Climbing Gym	Clothing Store	Cocktail Bar	Coffee Shop	Comedy Club	Comfort Food Restaurant	Concert Hall	Construction & Landscaping	Convenience Store	Convention Center	Cosmetics Shop	Cuban Restaurant	Cupcake Shop	Cycle Studio	Deli / Bodega	Department Store	Dessert Shop	Diner	Discount Store	Dive Bar	Doctor's Office	Dog Run	Donut Shop	Dry Cleaner	Dumpling Restaurant	Electronics Store	Falafel Restaurant	Farm	Farmers Market	Fast Food Restaurant	Fish Market	Flower Shop	Food	Food & Drink Shop	Food Court	Food Service	Food Truck	French Restaurant	Fried Chicken Joint	Frozen Yogurt Shop	Furniture / Home Store	Garden	Gas Station	Gastropub	Gay Bar	Gift Shop	Golf Course	Gourmet Shop	Greek Restaurant	Grocery Store	Gym	Gym / Fitness Center	Gym Pool	Harbor / Marina	Hardware Store	Historic Site	History Museum	Hockey Arena	Hockey Field	Hockey Rink	Home Service	Hookah Bar	Hostel	Hot Dog Joint	Hotel	Hotel Bar	Hotpot Restaurant	Ice Cream Shop	Indian Restaurant	Insurance Office	Intersection	Irish Pub	Italian Restaurant	Japanese Restaurant	Jewelry Store	Juice Bar	Kids Store	Korean Restaurant	Lake	Latin American Restaurant	Laundromat	Lawyer	Library	Lingerie Store	Liquor Store	Locksmith	Lounge	Market	Mediterranean Restaurant	Men's Store	Metro Station	Mexican Restaurant	Middle Eastern Restaurant	Miscellaneous Shop	Mobile Phone Shop	Monument / Landmark	Moroccan Restaurant	Movie Theater	Museum	Music Store	Music Venue	Nail Salon	National Park	New American Restaurant	Nightclub	Noodle House	Opera House	Optical Shop	Other Repair Shop	Outdoor Sculpture	Paper / Office Supplies Store	Park	Pastry Shop	Pedestrian Plaza	Performing Arts Venue	Pet Store	Pharmacy	Pizza Place	Planetarium	Platform	Playground	Plaza	Polish Restaurant	Pool	Pub	Record Shop	Recreation Center	Rental Car Location	Rental Service	Resort	Restaurant	River	Rock Club	Roof Deck	Salad Place	Salon / Barbershop	Sandwich Place	Scenic Lookout	Science Museum	Sculpture Garden	Seafood Restaurant	Shipping Store	Shoe Store	Shopping Mall	Skate Park	Skating Rink	Ski Chalet	Smoke Shop	Southern / Soul Food Restaurant	Souvenir Shop	Spa	Speakeasy	Sporting Goods Shop	Sports Bar	Steakhouse	Storage Facility	Street Food Gathering	Supermarket	Supplement Shop	Surf Spot	Sushi Restaurant	Taco Place	Tapas Restaurant	Tea Room	Tennis Court	Thai Restaurant	Theater	Thrift / Vintage Store	Tiki Bar	Tour Provider	Tourist Information Center	Track	Trail	Train Station	Tunnel	Vegetarian / Vegan Restaurant	Veterinarian	Video Store	Vietnamese Restaurant	Whisky Bar	Wine Bar	Wine Shop	Wings Joint	Women's Store
0	Allston	0.011494	0.000000	0.000000	0.011494	0.000000	0.011494	0.00	0.000000	0.000000	0.00	0.011494	0.022989	0.00	0.000000	0.000000	0.000000	0.045977	0.000000	0.034483	0.011494	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.011494	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.011494	0.011494	0.00	0.011494	0.000000	0.000000	0.000000	0.000000	0.011494	0.000000	0.000000	0.00	0.00000	0.000000	0.045977	0.00	0.00	0.00	0.000000	0.011494	0.00	0.022989	0.00	0.011494	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.011494	0.000000	0.022989	0.000000	0.000000	0.00	0.000000	0.011494	0.000000	0.011494	0.011494	0.011494	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.011494	0.011494	0.00	0.000000	0.00	0.011494	0.011494	0.011494	0.00	0.000000	0.022989	0.00	0.000000	0.0000	0.000000	0.000000	0.011494	0.000000	0.034483	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.011494	0.011494	0.000000	0.00	0.00	0.011494	0.000000	0.000000	0.011494	0.011494	0.011494	0.00	0.000000	0.00	0.011494	0.022989	0.00	0.000000	0.00	0.057471	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.022989	0.000000	0.000000	0.011494	0.011494	0.000000	0.000000	0.022989	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.057471	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.022989	0.00	0.000000	0.000000	0.022989	0.00000	0.000000	0.000000	0.011494	0.000000	0.011494	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.011494	0.00	0.00	0.000000	0.00	0.000000	0.00	0.011494	0.000000	0.000000	0.022989	0.011494	0.00	0.011494	0.000000	0.022989	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.011494	0.000000	0.000000	0.022989	0.00	0.000000	0.000000	0.000000	0.000000
1	Back Bay	0.010000	0.000000	0.020000	0.000000	0.000000	0.040000	0.00	0.000000	0.000000	0.00	0.000000	0.010000	0.00	0.000000	0.000000	0.000000	0.010000	0.010000	0.000000	0.000000	0.00	0.000000	0.000000	0.01	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.02	0.00	0.00	0.000000	0.020000	0.00	0.040000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.030000	0.000000	0.01	0.02000	0.010000	0.010000	0.020000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.00	0.01000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.01	0.000000	0.000000	0.010000	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.020000	0.010000	0.000000	0.030000	0.030000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.040000	0.000000	0.000000	0.040000	0.000000	0.00	0.000000	0.00	0.040000	0.000000	0.01	0.020000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.02	0.000000	0.000000	0.000000	0.010000	0.010000	0.020000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.010000	0.00	0.00	0.00	0.010000	0.000000	0.000000	0.00	0.0000	0.000000	0.020000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.020000	0.000000	0.000000	0.00	0.020000	0.010000	0.010000	0.01000	0.000000	0.000000	0.040000	0.000000	0.010000	0.020000	0.000000	0.000000	0.00	0.000000	0.010000	0.00	0.030000	0.00	0.02	0.000000	0.02	0.000000	0.00	0.000000	0.000000	0.000000	0.010000	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.000000	0.00	0.01	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.000000	0.000000	0.010000
2	Bay Village	0.010000	0.000000	0.000000	0.000000	0.000000	0.020000	0.00	0.000000	0.000000	0.01	0.040000	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.010000	0.010000	0.01	0.000000	0.00	0.000000	0.010000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.040000	0.00	0.00	0.00	0.000000	0.010000	0.01	0.060000	0.01	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.01	0.02000	0.010000	0.000000	0.020000	0.000000	0.000000	0.010000	0.00	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.01000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.02	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.00	0.000000	0.0000	0.010000	0.000000	0.010000	0.030000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.01	0.000000	0.020000	0.000000	0.010000	0.000000	0.000000	0.01	0.000000	0.00	0.050000	0.010000	0.01	0.010000	0.00	0.010000	0.010000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.01	0.00	0.00	0.01	0.000000	0.020000	0.00	0.00	0.02	0.010000	0.000000	0.010000	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.00	0.000000	0.010000	0.040000	0.01000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.060000	0.01	0.00	0.000000	0.02	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.050000	0.000000	0.01	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.020000	0.00	0.010000	0.020000	0.000000	0.000000
3	Beacon Hill	0.010000	0.000000	0.000000	0.000000	0.000000	0.040000	0.00	0.000000	0.000000	0.00	0.010000	0.010000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.020000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.01	0.00000	0.000000	0.00	0.000000	0.000000	0.010000	0.00	0.000000	0.00	0.000000	0.010000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.010000	0.01	0.040000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.01000	0.010000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.020000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.02	0.000000	0.000000	0.000000	0.01	0.000000	0.010000	0.00	0.010000	0.0000	0.010000	0.010000	0.010000	0.010000	0.030000	0.000000	0.000000	0.000000	0.030000	0.010000	0.010000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.040000	0.020000	0.010000	0.010000	0.000000	0.00	0.000000	0.01	0.060000	0.000000	0.00	0.010000	0.01	0.000000	0.010000	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.00	0.010000	0.000000	0.000000	0.020000	0.00	0.000000	0.01	0.01	0.01	0.01	0.000000	0.020000	0.00	0.00	0.00	0.000000	0.000000	0.040000	0.00	0.0000	0.010000	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.020000	0.010000	0.000000	0.00	0.010000	0.000000	0.020000	0.01000	0.000000	0.000000	0.030000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.03	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.00	0.010000	0.000000	0.000000	0.010000	0.000000	0.00	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
4	Brighton	0.000000	0.000000	0.000000	0.000000	0.000000	0.012658	0.00	0.000000	0.000000	0.00	0.000000	0.012658	0.00	0.000000	0.000000	0.000000	0.037975	0.025316	0.025316	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.012658	0.000000	0.000000	0.00	0.000000	0.012658	0.000000	0.000000	0.000000	0.037975	0.000000	0.000000	0.00	0.00000	0.000000	0.037975	0.00	0.00	0.00	0.000000	0.000000	0.00	0.050633	0.00	0.000000	0.00	0.000000	0.037975	0.00	0.000000	0.000000	0.00	0.00000	0.025316	0.000000	0.012658	0.000000	0.000000	0.000000	0.00	0.012658	0.037975	0.012658	0.000000	0.012658	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.012658	0.0000	0.000000	0.025316	0.037975	0.012658	0.012658	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.012658	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.012658	0.012658	0.000000	0.000000	0.000000	0.000000	0.012658	0.000000	0.000000	0.00	0.000000	0.00	0.012658	0.000000	0.000000	0.000000	0.00	0.012658	0.00	0.00	0.00	0.00	0.012658	0.012658	0.00	0.00	0.00	0.012658	0.025316	0.075949	0.00	0.0000	0.012658	0.000000	0.000000	0.000000	0.050633	0.000000	0.000000	0.000000	0.000000	0.00	0.012658	0.000000	0.000000	0.00	0.000000	0.012658	0.025316	0.00000	0.000000	0.000000	0.000000	0.000000	0.012658	0.000000	0.000000	0.000000	0.00	0.012658	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.012658	0.00	0.000000	0.012658	0.000000	0.025316	0.000000	0.00	0.000000	0.000000	0.025316	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.012658	0.000000	0.000000	0.012658	0.000000	0.000000	0.000000	0.00	0.000000	0.012658	0.012658	0.000000
5	Charlestown	0.000000	0.000000	0.000000	0.000000	0.000000	0.014493	0.00	0.014493	0.000000	0.00	0.000000	0.014493	0.00	0.000000	0.000000	0.000000	0.000000	0.014493	0.014493	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.014493	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.028986	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.043478	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.028986	0.00	0.000000	0.00	0.000000	0.028986	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.014493	0.043478	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.014493	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.043478	0.00	0.000000	0.0000	0.000000	0.000000	0.043478	0.028986	0.000000	0.014493	0.014493	0.000000	0.000000	0.043478	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.014493	0.000000	0.000000	0.014493	0.000000	0.00	0.000000	0.00	0.014493	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.014493	0.000000	0.00	0.014493	0.000000	0.014493	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014493	0.014493	0.00	0.000000	0.00	0.014493	0.000000	0.028986	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.057971	0.00	0.00	0.00	0.014493	0.014493	0.043478	0.00	0.0000	0.014493	0.014493	0.000000	0.014493	0.028986	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.014493	0.00000	0.000000	0.000000	0.014493	0.000000	0.000000	0.014493	0.014493	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.014493	0.000000	0.000000	0.00	0.000000	0.014493	0.014493	0.014493	0.000000	0.00	0.00	0.014493	0.000000	0.000000	0.000000	0.014493	0.000000	0.000000	0.000000	0.000000	0.00	0.014493	0.000000	0.000000	0.000000
6	Chinatown	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.070000	0.010000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.010000	0.00	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.020000	0.000000	0.000000	0.00	0.00000	0.000000	0.040000	0.00	0.00	0.00	0.000000	0.010000	0.01	0.060000	0.01	0.000000	0.00	0.000000	0.000000	0.00	0.010000	0.000000	0.00	0.00000	0.010000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.02	0.000000	0.000000	0.000000	0.01	0.000000	0.020000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.010000	0.020000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.01	0.000000	0.030000	0.010000	0.010000	0.000000	0.000000	0.00	0.000000	0.00	0.030000	0.010000	0.00	0.000000	0.00	0.010000	0.010000	0.000000	0.000000	0.000000	0.010000	0.00	0.010000	0.000000	0.010000	0.010000	0.010000	0.000000	0.000000	0.010000	0.010000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.00	0.000000	0.01	0.00	0.00	0.02	0.000000	0.020000	0.00	0.00	0.02	0.000000	0.000000	0.020000	0.00	0.0000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.00	0.020000	0.010000	0.060000	0.01000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.020000	0.00	0.00	0.000000	0.02	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.00	0.010000	0.000000	0.000000	0.030000	0.000000	0.00	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.020000	0.00	0.010000	0.010000	0.000000	0.000000
7	Dorchester	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.062500	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.062500	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.000000	0.000000	0.062500	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.062500	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.062500	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.0625	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.062500	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.062500	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.062500	0.00	0.00	0.00	0.000000	0.125000	0.125000	0.00	0.0625	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.062500	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.062500	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
8	Downtown	0.000000	0.000000	0.000000	0.000000	0.000000	0.040000	0.01	0.000000	0.000000	0.00	0.010000	0.000000	0.00	0.000000	0.010000	0.000000	0.020000	0.010000	0.050000	0.000000	0.00	0.000000	0.000000	0.00	0.01	0.000000	0.000000	0.00	0.00000	0.000000	0.01	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.010000	0.010000	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.060000	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.01	0.030000	0.00	0.000000	0.01	0.000000	0.010000	0.01	0.000000	0.000000	0.00	0.00000	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000	0.00	0.000000	0.050000	0.000000	0.000000	0.000000	0.000000	0.01	0.00000	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.01	0.010000	0.0000	0.010000	0.000000	0.010000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.020000	0.010000	0.000000	0.000000	0.000000	0.01	0.00	0.010000	0.010000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.00	0.030000	0.020000	0.00	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.030000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.01	0.000000	0.00	0.00	0.00	0.00	0.000000	0.010000	0.00	0.01	0.00	0.000000	0.020000	0.030000	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.010000	0.000000	0.00	0.020000	0.000000	0.040000	0.00	0.000000	0.000000	0.060000	0.00000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.01	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.010000	0.000000	0.01	0.000000	0.000000	0.010000	0.010000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.010000	0.000000	0.010000	0.000000
9	East Boston	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.023256	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.023256	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.023256	0.00	0.00000	0.000000	0.00	0.000000	0.023256	0.000000	0.00	0.000000	0.00	0.023256	0.000000	0.000000	0.000000	0.00	0.000000	0.023256	0.000000	0.000000	0.000000	0.046512	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.023256	0.000000	0.023256	0.000000	0.000000	0.000000	0.00	0.000000	0.023256	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.023256	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.023256	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.023256	0.000000	0.000000	0.023256	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.023256	0.00	0.000000	0.00	0.069767	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.069767	0.000000	0.000000	0.000000	0.00	0.046512	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.116279	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.093023	0.00	0.00	0.00	0.000000	0.000000	0.069767	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.023256	0.000000	0.000000	0.000000	0.000000	0.00	0.023256	0.000000	0.000000	0.00	0.000000	0.000000	0.046512	0.00000	0.000000	0.000000	0.046512	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.023256	0.00	0.000000	0.000000	0.000000	0.000000
10	Fenway	0.010000	0.000000	0.000000	0.000000	0.000000	0.050000	0.00	0.020000	0.020000	0.00	0.000000	0.000000	0.00	0.000000	0.010000	0.000000	0.020000	0.000000	0.020000	0.030000	0.01	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.01000	0.000000	0.00	0.000000	0.000000	0.010000	0.00	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.00	0.030000	0.000000	0.000000	0.000000	0.000000	0.040000	0.000000	0.000000	0.00	0.00000	0.000000	0.010000	0.00	0.00	0.00	0.000000	0.000000	0.01	0.010000	0.00	0.000000	0.01	0.000000	0.000000	0.00	0.010000	0.000000	0.00	0.01000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.020000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.030000	0.02	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.020000	0.010000	0.020000	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.030000	0.000000	0.000000	0.010000	0.010000	0.00	0.000000	0.00	0.010000	0.020000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.030000	0.000000	0.010000	0.000000	0.000000	0.020000	0.020000	0.010000	0.000000	0.000000	0.000000	0.01	0.010000	0.01	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.020000	0.00	0.01	0.00	0.000000	0.000000	0.030000	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.030000	0.000000	0.000000	0.01	0.010000	0.000000	0.020000	0.00000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.01	0.010000	0.00	0.01	0.010000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.030000	0.000000	0.000000	0.00	0.01	0.000000	0.000000	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000	0.020000	0.00	0.000000	0.000000	0.000000	0.000000
11	Hyde Park	0.000000	0.000000	0.000000	0.000000	0.000000	0.060606	0.00	0.000000	0.000000	0.00	0.000000	0.030303	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.030303	0.030303	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.030303	0.000000	0.030303	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.030303	0.00	0.000000	0.00	0.000000	0.00	0.030303	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.000000	0.000000	0.030303	0.000000	0.00	0.000000	0.030303	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.030303	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.090909	0.030303	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.060606	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.030303	0.000000	0.00	0.000000	0.00	0.030303	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.060606	0.00	0.00	0.00	0.000000	0.060606	0.121212	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.030303	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.030303	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.030303	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.030303	0.030303	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
12	Jamaica Plain	0.034483	0.000000	0.034483	0.000000	0.000000	0.034483	0.00	0.034483	0.000000	0.00	0.017241	0.000000	0.00	0.000000	0.000000	0.000000	0.068966	0.017241	0.017241	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.034483	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.017241	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.017241	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.051724	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.017241	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.017241	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.017241	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.034483	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.017241	0.000000	0.017241	0.000000	0.017241	0.00	0.017241	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.017241	0.017241	0.017241	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.034483	0.00	0.017241	0.00	0.00	0.00	0.00	0.000000	0.086207	0.00	0.00	0.00	0.017241	0.017241	0.051724	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.034483	0.017241	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.017241	0.00000	0.000000	0.000000	0.017241	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.017241	0.00	0.000000	0.000000	0.017241	0.017241	0.034483	0.00	0.00	0.000000	0.000000	0.017241	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
13	Kenmore	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.50	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.000000	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
14	Longwood	0.000000	0.000000	0.000000	0.000000	0.010000	0.040000	0.00	0.020000	0.030000	0.00	0.000000	0.000000	0.00	0.000000	0.010000	0.010000	0.010000	0.000000	0.010000	0.010000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.01000	0.000000	0.00	0.000000	0.000000	0.010000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.070000	0.010000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.060000	0.00	0.000000	0.00	0.000000	0.010000	0.00	0.010000	0.000000	0.00	0.01000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.030000	0.000000	0.000000	0.000000	0.020000	0.00	0.00000	0.010000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.010000	0.030000	0.01	0.000000	0.010000	0.00	0.000000	0.0000	0.000000	0.030000	0.010000	0.030000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.020000	0.000000	0.00	0.000000	0.00	0.020000	0.010000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.030000	0.020000	0.000000	0.000000	0.000000	0.000000	0.01	0.010000	0.00	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.040000	0.00	0.00	0.00	0.000000	0.000000	0.060000	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.030000	0.000000	0.000000	0.00	0.010000	0.000000	0.040000	0.00000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.01	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.00	0.000000	0.000000	0.030000	0.010000	0.000000	0.00	0.00	0.000000	0.010000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
15	Mattapan	0.000000	0.000000	0.000000	0.000000	0.000000	0.045455	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.045455	0.000000	0.045455	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.090909	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.045455	0.00	0.000000	0.045455	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.045455	0.000000	0.000000	0.000000	0.045455	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.045455	0.045455	0.00	0.0000	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.00	0.000000	0.045455	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.045455	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.045455
16	Mission Hill	0.000000	0.000000	0.000000	0.000000	0.014286	0.014286	0.00	0.014286	0.042857	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.014286	0.000000	0.000000	0.014286	0.014286	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.014286	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.014286	0.014286	0.042857	0.014286	0.000000	0.071429	0.014286	0.000000	0.00	0.00000	0.000000	0.014286	0.00	0.00	0.00	0.000000	0.000000	0.00	0.042857	0.00	0.000000	0.00	0.000000	0.014286	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.014286	0.085714	0.000000	0.000000	0.000000	0.028571	0.00	0.00000	0.014286	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.014286	0.00	0.000000	0.014286	0.014286	0.00	0.000000	0.014286	0.00	0.000000	0.0000	0.000000	0.014286	0.000000	0.028571	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.014286	0.000000	0.000000	0.014286	0.000000	0.00	0.014286	0.00	0.028571	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.014286	0.000000	0.000000	0.000000	0.000000	0.000000	0.014286	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.014286	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.042857	0.00	0.00	0.00	0.000000	0.000000	0.042857	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.014286	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.014286	0.000000	0.00	0.000000	0.000000	0.057143	0.00000	0.000000	0.000000	0.000000	0.014286	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.014286	0.000000	0.000000	0.028571	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.014286	0.000000	0.000000	0.000000	0.014286	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
17	North End	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.050000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.01	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.030000	0.000000	0.000000	0.01	0.00000	0.000000	0.000000	0.01	0.01	0.00	0.000000	0.000000	0.00	0.020000	0.01	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.010000	0.000000	0.010000	0.010000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.01000	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.010000	0.00	0.000000	0.0000	0.010000	0.000000	0.010000	0.010000	0.000000	0.000000	0.000000	0.000000	0.030000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.340000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.010000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.01	0.000000	0.060000	0.01	0.00	0.00	0.000000	0.000000	0.050000	0.00	0.0000	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.000000	0.00	0.000000	0.010000	0.020000	0.00000	0.000000	0.000000	0.050000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.00	0.010000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.010000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.020000	0.000000	0.000000
18	Roslindale	0.048780	0.000000	0.000000	0.000000	0.000000	0.073171	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.024390	0.024390	0.048780	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.02439	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.02439	0.000000	0.024390	0.00	0.00	0.00	0.000000	0.000000	0.00	0.024390	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.02439	0.024390	0.000000	0.000000	0.000000	0.024390	0.000000	0.00	0.000000	0.024390	0.000000	0.000000	0.000000	0.000000	0.00	0.02439	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.024390	0.073171	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.024390	0.024390	0.00	0.000000	0.00	0.048780	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.024390	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.024390	0.097561	0.00	0.0000	0.000000	0.048780	0.000000	0.024390	0.024390	0.000000	0.000000	0.024390	0.000000	0.00	0.024390	0.000000	0.000000	0.00	0.000000	0.000000	0.048780	0.02439	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
19	Roxbury	0.000000	0.000000	0.000000	0.000000	0.026316	0.000000	0.00	0.026316	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.026316	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.026316	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.026316	0.000000	0.000000	0.026316	0.000000	0.000000	0.026316	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.052632	0.00	0.000000	0.026316	0.00	0.00000	0.000000	0.000000	0.000000	0.026316	0.000000	0.000000	0.00	0.000000	0.131579	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.026316	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.026316	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.026316	0.00	0.052632	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.026316	0.000000	0.000000	0.000000	0.000000	0.000000	0.026316	0.000000	0.000000	0.000000	0.026316	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.026316	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.026316	0.00	0.00	0.00	0.000000	0.026316	0.078947	0.00	0.0000	0.000000	0.026316	0.000000	0.000000	0.000000	0.026316	0.026316	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.026316	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.052632	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.00	0.026316	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.026316	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
20	South Boston	0.000000	0.013699	0.000000	0.000000	0.000000	0.013699	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.013699	0.013699	0.068493	0.000000	0.00	0.000000	0.027397	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.013699	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.013699	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.013699	0.00	0.00	0.00	0.013699	0.000000	0.00	0.041096	0.00	0.000000	0.00	0.000000	0.027397	0.00	0.013699	0.000000	0.00	0.00000	0.000000	0.000000	0.013699	0.013699	0.000000	0.027397	0.00	0.013699	0.054795	0.013699	0.000000	0.000000	0.000000	0.00	0.00000	0.013699	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.013699	0.000000	0.00	0.000000	0.0000	0.013699	0.000000	0.013699	0.041096	0.027397	0.000000	0.000000	0.000000	0.013699	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.013699	0.000000	0.00	0.000000	0.00	0.041096	0.000000	0.00	0.013699	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.013699	0.000000	0.000000	0.013699	0.000000	0.000000	0.000000	0.013699	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.013699	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.027397	0.00	0.00	0.00	0.000000	0.013699	0.082192	0.00	0.0000	0.000000	0.000000	0.013699	0.000000	0.000000	0.000000	0.000000	0.013699	0.013699	0.00	0.000000	0.000000	0.000000	0.00	0.013699	0.000000	0.054795	0.00000	0.000000	0.000000	0.013699	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.027397	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.013699	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.013699	0.00	0.00	0.000000	0.013699	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
21	South Boston Waterfront	0.000000	0.000000	0.000000	0.000000	0.000000	0.010000	0.00	0.010000	0.020000	0.00	0.040000	0.000000	0.01	0.000000	0.010000	0.000000	0.020000	0.000000	0.030000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.010000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.01	0.000000	0.000000	0.030000	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.01	0.040000	0.01	0.000000	0.00	0.000000	0.010000	0.00	0.000000	0.000000	0.00	0.01000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.01	0.010000	0.02	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.030000	0.000000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.090000	0.010000	0.000000	0.010000	0.000000	0.00	0.000000	0.00	0.060000	0.000000	0.00	0.010000	0.00	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.020000	0.000000	0.000000	0.030000	0.000000	0.000000	0.000000	0.000000	0.000000	0.01	0.010000	0.00	0.010000	0.000000	0.000000	0.020000	0.01	0.000000	0.00	0.01	0.00	0.00	0.000000	0.020000	0.00	0.00	0.00	0.000000	0.010000	0.010000	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.020000	0.000000	0.000000	0.00	0.020000	0.000000	0.010000	0.02000	0.000000	0.000000	0.080000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.01	0.00	0.000000	0.05	0.000000	0.00	0.000000	0.000000	0.000000	0.010000	0.010000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
22	South End	0.000000	0.000000	0.000000	0.000000	0.000000	0.046512	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.023256	0.023256	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.046512	0.000000	0.000000	0.000000	0.000000	0.023256	0.000000	0.046512	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.00	0.046512	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00000	0.023256	0.000000	0.000000	0.000000	0.023256	0.000000	0.00	0.000000	0.046512	0.000000	0.000000	0.000000	0.000000	0.00	0.00000	0.023256	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.046512	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.000000	0.023256	0.023256	0.000000	0.000000	0.000000	0.000000	0.000000	0.046512	0.000000	0.000000	0.023256	0.00	0.00	0.000000	0.046512	0.023256	0.000000	0.023256	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.023256	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.023256	0.000000	0.000000	0.000000	0.000000	0.00	0.023256	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.069767	0.00	0.0000	0.000000	0.000000	0.000000	0.000000	0.023256	0.000000	0.000000	0.023256	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.046512	0.00000	0.023256	0.023256	0.000000	0.046512	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.046512	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
23	West End	0.020000	0.000000	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.070000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.01	0.00000	0.000000	0.00	0.000000	0.010000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.01	0.00000	0.000000	0.000000	0.01	0.01	0.01	0.000000	0.000000	0.00	0.040000	0.01	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.01000	0.020000	0.000000	0.000000	0.010000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.01000	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.02	0.000000	0.000000	0.000000	0.00	0.000000	0.010000	0.00	0.000000	0.0000	0.020000	0.010000	0.010000	0.000000	0.010000	0.000000	0.000000	0.000000	0.030000	0.010000	0.010000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.030000	0.020000	0.010000	0.010000	0.000000	0.00	0.000000	0.00	0.080000	0.000000	0.00	0.010000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.010000	0.00	0.000000	0.000000	0.000000	0.020000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.01	0.01	0.01	0.000000	0.050000	0.00	0.00	0.00	0.000000	0.000000	0.050000	0.01	0.0000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.020000	0.000000	0.000000	0.00	0.010000	0.010000	0.020000	0.00000	0.020000	0.000000	0.040000	0.000000	0.010000	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.010000	0.01	0.000000	0.01	0.000000	0.000000	0.000000	0.010000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000
24	West Roxbury	0.000000	0.000000	0.000000	0.000000	0.000000	0.038462	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.019231	0.019231	0.019231	0.019231	0.038462	0.019231	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.019231	0.000000	0.000000	0.00	0.00000	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.019231	0.00	0.019231	0.00	0.000000	0.00	0.000000	0.057692	0.00	0.000000	0.000000	0.00	0.00000	0.019231	0.000000	0.000000	0.019231	0.019231	0.000000	0.00	0.000000	0.019231	0.000000	0.000000	0.019231	0.000000	0.00	0.00000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.019231	0.000000	0.00	0.000000	0.0000	0.000000	0.000000	0.038462	0.019231	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.019231	0.00	0.000000	0.00	0.019231	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.057692	0.019231	0.000000	0.000000	0.019231	0.000000	0.000000	0.019231	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.019231	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.00	0.00	0.000000	0.038462	0.00	0.00	0.00	0.000000	0.076923	0.076923	0.00	0.0000	0.000000	0.019231	0.000000	0.000000	0.019231	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.019231	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.019231	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.019231	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.019231	0.000000	0.000000	0.019231	0.019231	0.019231	0.00	0.000000	0.000000	0.000000	0.000000